Expensive Weddings

the dumbell chart

Photo by Khadija Yousaf on Unsplash

A wedding gown entails multilayering of expensive specialty fabrics for an outfit
whose useful lifespan may come and go in a single afternoon…
— Mary Roach

Millions of weddings take place every month, and couples across the world find themselves with more and more choices for when, where and how to celebrate their love. The Knot, WeddingWire and Bodas.net teamed up to gather feedback from 20,000 couples in 14 countries to understand how today’s couples are planning their wedding. Let’s explore using a dumbbell chart to illustrate some of the findings.

Ingest

country, vendor, percent

df <- read.csv("archetypes/expensive-weddings/expensive-weddings.csv", header = TRUE, stringsAsFactors = FALSE, encoding = "UTF-8")
df

# to make emoji map
# unique(df$Vendor)

Wrangle

enrich with emoji map

Analyze

summarize for min and max (to use as segment points)

df_wo_alcohol <- filter(df, Vendor != "Serve alcohol")

df_segment <- df_wo_alcohol %>%
  select(Country, Percent) %>%
  group_by(Country) %>%
  summarise(
    min_pt = min(Percent, na.rm = T),
    max_pt = max(Percent, na.rm = T)
)

df_segment

View, chart

emoji wedding expenses

theme_opts <- theme(
    text = element_text(family = "inconsolata"), 
    plot.title = element_text(color = "black", size = 14, face = "bold"),
    plot.subtitle = element_text(color = "black", size = 12),
    plot.caption = element_text(color = "#555555", size = 10, hjust = 1.0, vjust = 1.0),
    axis.title.x = element_blank(),
    axis.text.x = element_text(size = 12, hjust = 0.5, vjust = 0.5),
    axis.text.y = element_text(size = 12, hjust = 1.0, vjust = 0.5),
    panel.border = element_blank(),
    panel.background=element_rect(fill="#FDFDFD", colour="NA"),
    panel.grid.minor = element_blank(), # remove minor gridlines
    panel.grid.major = element_line(size = 0.5, linetype = 'dotted', colour = "#999999"),
    legend.title = element_blank(), # remove legend title
    legend.text = element_text(color = "black", size = 10),
    legend.position='bottom',
    plot.background = element_rect(fill="#FDFDFD", colour="NA"),
    plot.margin = unit(c(0.5, 0.5, 0.5, 0.25), "in")
  )

v1 <- ggplot() +  
  geom_segment(data = df_segment, aes(x = min_pt, xend = max_pt, y = Country, yend = Country), lineend = "butt", linejoin = "round", size=1.0, color="#F44336" ) +
  geom_point(data = df_wrangle, aes(x = Percent, y = Country), shape = 21, size = 16, color="black", fill="white", alpha = 0.9 ) +
  geom_text(data = df_wrangle, aes(x = Percent, y = Country, label = emoji(Emoji)), size = 9, vjust = 0.25, family="EmojiOne") +
  scale_x_continuous(labels=scales::percent_format(scale = 1), sec.axis = dup_axis()) +
  coord_cartesian(clip = "off") +
  labs(title = "GLOBAL WEDDING REPORT",
       subtitle = "percentage reporting, category of expense and serving alcohol",
       caption = "Source: the knot, weddingwire, bodas.net",
       x = NULL,
       y = NULL) +
  theme_minimal() + 
  theme_opts  

girafe(ggobj = v1, width_svg = 1280/72, height_svg = 720/72,
       options = list(opts_sizing(rescale = TRUE, width = 1.0)))

References

citations for narrative and data sources

Inspiration: The Economist, Why expensive weddings are a bad idea, https://www.economist.com/graphic-detail/2018/05/19/why-expensive-weddings-are-a-bad-idea Data: World Wedding Report 2019, https://cdn1.weddingwire.com/assets/docs/GlobalWeddingReport/TKWW_Global_Wedding_Report_2019_us_F4.pdf